Digital watermarking has been increasingly popular in tagging and tracking goods, especially in a retail environment. Digital watermarking (DWM) technology is based upon digitally embedding—i.e. watermarking-tags or any other type of identification information within other images. For example, multiple copies of a barcode or other machine-readable indicia may be digitally watermarked within images, texture, and/or text in a package containing a product. A digital watermark decoder may decode digital watermarks from images, texture, and/or text to retrieve the watermarked barcodes. The barcodes may be read by a barcode reader.
A digital watermark, by definition, is hidden from plain view. Furthermore, a digital watermark does not have a finite pattern as other tags, such as a barcode. Therefore, a watermark decoder has to analyze each part of an image frame to determine whether there is a digital watermark in that part of the image frame. Image processing is computation intensive, and therefore analyzing each and every part of multiple image frames is computationally not efficient. Such brute-force analysis requires a lot of computing power and time. Current decode strategy for DWM decoding is based on static and fixed position for candidate regions, which is not optimal and uses a lot of processing resources for each processed image frame. A time consuming decoding process may not be applicable in a retail environment, where the checkout process has to be fast and efficient.
Therefore, conventional technology for digital watermark decoding may be slow and may not be viable in a retail environment. As such, a significant improvement in the digital watermark decoding technology to make it fast, efficient, and applicable in a retail and industrial environment is desirable.